Wireless sensor networks may typically involve hundreds or thousands of small, preferably inexpensive sensor devices or nodes that can remotely communicate with neighboring devices such as over a wireless communication link within a limited radio range. The devices typically include a sensing ability, computational ability, and bi-directional wireless communications ability, and may also include an integrated power supply. By relaying information to each other, sensors operate to transmit signals to a command post or central network sensor or gateway which may be located anywhere within the network.
Wireless sensors are used to detect any of a variety of parameters, including for example: environmental; motion or force; electromagnetic; and chemical or biological data. They are used in a wide variety of applications such as: agriculture; weather; aerospace; military; environment or industrial control and monitoring; wildlife monitoring; security monitoring; inventory control; and many others.
In order to make the use of the data collected by the sensors practical, it is desirable to know the location of each sensor in the network. Known sensor localization methods include use of the global navigation satellite systems (“GNSS”), triangulation-based multidimensional scaling, convex optimization, and semidefinite programming (“SDP”) relaxation, for example.
Localization based on GNSS, such as the global positioning system (“GPS”), the global navigation satellite system (“GLONASS”), and the Galileo positioning system for example, suffers from many potential drawbacks. For example, a GNSS-based localization system is typically expensive to deploy because the devices employing GNSS technology are relatively expensive. In addition, GNSS typically has limited accuracy in determining position. Without the use of specialized equipment, normal GNSS can determine subject locations with approximately five-meter accuracy in ideal reception conditions. In addition, GNSS position localization requires line of sight reception of satellite signals, thus limiting its use to an outdoor environment. GNSS systems are typically also limited in effectiveness when obstacles block line of sight to satellite positions such as in urban, steep terrain or forested locations. Furthermore, for certain applications that require high security, GNSS systems may compromise security requirements through their use of public satellite communications. Moreover, due to satellite communication delay, use of GNSS might not be an effective method for real-time tracking of moving sensors, particularly in some environments where GNSS reception is less than ideal.
Conventional non-GPS-based wireless positioning systems and methods also suffer from various drawbacks. One significant such drawback is that prior methods are often not suitable for deployment in large-scale networks, as their performance typically deteriorates rapidly as the network increases in size. The execution times required for sensor system operation and localization may not typically be fast enough for real-time applications, for example.
Disturbances in an environment of interest in which a wireless sensor network is deployed may affect the transmission and/or reception of wireless signals, such as unevenness and geographical obstacles in a terrain. A conventional approach to account for the geographical or other non-uniformity in the radio space in which a wireless sensor network is deployed may typically rely on manual system calibration by placing a mobile radio transceiver at selected locations within the wireless sensor network, taking measurements related to signal strengths of the mobile radio transceiver to establish calibration standards, and subsequently calibrating system-generated localization results based on the calibration standards.
In order for a wireless sensor network to allow location aware services, such as localization and intrusion detection, sensor nodes must be distributed in the target environment at specific locations during system setup in order to allow for spatially distributed sensing and detection across the target environment. A conventional approach is to plan the location of each particular sensor node (deployment mapping) and then follow the map layout during sensor nodes installation.